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Functions335 in github.com/SamvitJ/ReXCam

⨍ Functions 335◇ Types & classes 116

↓ 1 callersFunctionse_resnext50_32x4d

(num_classes=1000, pretrained='imagenet')

models/SEResNet.py:424

↓ 1 callersFunctiontrain

(epoch, model, criterion, optimizer, trainloader, use_gpu)

train_img_model_xent.py:206

↓ 1 callersFunctiontrain

(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu)

train_img_model_xent_htri.py:206

↓ 1 callersFunctiontrain

(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu)

train_vid_model_xent_htri.py:203

↓ 1 callersFunctiontrain

(epoch, model, criterion, optimizer, trainloader, use_gpu)

train_vid_model_xent.py:194

↓ 1 callersFunctiontrain

(epoch, model, criterion_xent, criterion_ring, optimizer, trainloader, use_gpu)

train_img_model_ring.py:202

↓ 1 callersFunctiontrain

(epoch, model, criterion_xent, criterion_cent, optimizer_model, optimizer_cent, trainloader, use_gpu)

train_img_model_cent.py:202

Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image.

transforms.py:23

Method__enter__

Method__getitem__

dataset_loader.py:34

Method__getitem__

dataset_loader.py:51

Method__getitem__

dataset_loader.py:74

(self, root='data', **kwargs)

data_manager.py:34

(self, root='data', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, **kwargs)

data_manager.py:125

(self, root='data', **kwargs)

data_manager.py:381

(self, root='data', **kwargs)

data_manager.py:464

(self, root='data', split_id=0, **kwargs)

data_manager.py:549

(self, root='data', split_id=0, **kwargs)

data_manager.py:692

(self, root='data', split_id=0, **kwargs)

data_manager.py:845

(self, root='data', split_id=0, min_seq_len=0, **kwargs)

data_manager.py:980

(self, root='data', split_id=0, **kwargs)

data_manager.py:1126

(self, root='data', min_seq_len=0, **kwargs)

data_manager.py:1287

(self, root='data', split_id=0, **kwargs)

data_manager.py:1423

(self, root='data', split_id=0, min_seq_len=0, **kwargs)

data_manager.py:1594

(self, root='data', min_seq_len=0, **kwargs)

data_manager.py:1694

(self, data_source, num_instances=4)

(self, height, width, p=0.5, interpolation=Image.BILINEAR)

transforms.py:17

(self, fpath=None)

(self, num_classes, epsilon=0.1, use_gpu=True)

(self, margin=0.3)

(self, num_classes=10, feat_dim=2, use_gpu=True)

(self, dataset, transform=None)

dataset_loader.py:27

(self, dataset, transform=None)

dataset_loader.py:44

(self, dataset, seq_len=15, sample='evenly', transform=None)

dataset_loader.py:65

(self, num_classes, loss={'xent'}, **kwargs)

models/DenseNet.py:11

(self, in_chs, activation_fn=nn.ReLU(inplace=True))

models/DPN.py:215

(self, in_chs, out_chs, kernel_size, stride, padding=0, groups=1, activation_fn=nn.ReLU(inpla

models/DPN.py:226

(self, num_init_features, kernel_size=7, padding=3, activation_fn=nn.ReLU(inplace=True))

models/DPN.py:238

( self, in_chs, num_1x1_a, num_3x3_b, num_1x1_c, inc, groups, block_type='normal', b=False)

models/DPN.py:256

(self, output_size=1, pool_type='avg')

models/DPN.py:471

(self, in_c, out_c, k, s, p)

models/MuDeep.py:21

models/MuDeep.py:31

models/MuDeep.py:45

models/MuDeep.py:72

models/MuDeep.py:91

(self, num_classes, loss={'xent'}, **kwargs)

models/MuDeep.py:145

models/NASNet.py:56

(self, stride=2, padding=1)

models/NASNet.py:70

(self, in_channels, out_channels, dw_kernel, dw_stride, dw_padding, bias=False)

models/NASNet.py:84

(self, in_channels, out_channels, kernel_size, stride, padding, name=None, bias=False)

models/NASNet.py:101

(self, in_channels, out_channels, kernel_size, stride, padding, bias=False)

models/NASNet.py:128

(self, in_channels, out_channels, kernel_size, stride, padding, z_padding=1, bias=False)

models/NASNet.py:149

(self, stem_filters, num_filters)

models/NASNet.py:217

(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right)

models/NASNet.py:293

(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right)

models/NASNet.py:362

(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right)

models/NASNet.py:415

(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right)

models/NASNet.py:470

(self, num_classes, stem_filters=32, penultimate_filters=1056, filters_multiplier=2, loss={'xent'}, **kwargs)

models/NASNet.py:529

(self, num_groups)

models/ShuffleNet.py:11

(self, num_classes, loss={'xent'}, num_groups=3, **kwargs)

models/ShuffleNet.py:70

(self, in_c, out_c, k, s=1, p=0)

models/HACNN.py:21

(self, in_channels, out_channels)

models/HACNN.py:35

(self, in_channels, out_channels)

models/HACNN.py:70

models/HACNN.py:97

(self, in_channels, reduction_rate=16)

models/HACNN.py:115

(self, in_channels)

models/HACNN.py:134

(self, in_channels)

models/HACNN.py:149

(self, in_channels)

models/HACNN.py:168

(self, in_planes, out_planes, kernel_size, stride, padding=0)

models/InceptionResNetV2.py:42

models/InceptionResNetV2.py:62

(self, scale=1.0)

models/InceptionResNetV2.py:94

models/InceptionResNetV2.py:128

(self, scale=1.0)

models/InceptionResNetV2.py:151

models/InceptionResNetV2.py:179

(self, num_classes, loss={'xent'}, **kwargs)

models/InceptionResNetV2.py:271

(self, channels, reduction)

models/SEResNet.py:90

(self, inplanes, planes, groups, reduction, stride=1, downsample=None)

models/SEResNet.py:141

(self, inplanes, planes, groups, reduction, stride=1, downsample=None)

models/SEResNet.py:167

(self, inplanes, planes, groups, reduction, stride=1, downsample=None, base_width=4)

models/SEResNet.py:190

(self, num_classes, loss={'xent'}, **kwargs)

models/SEResNet.py:448

(self, num_classes, loss={'xent'}, **kwargs)

models/SEResNet.py:476

(self, num_classes, loss={'xent'}, **kwargs)

models/SEResNet.py:504

(self, num_classes, loss={'xent'}, **kwargs)

models/SEResNet.py:532

(self, in_planes, out_planes, kernel_size, stride, padding=0)

models/InceptionV4.py:42

models/InceptionV4.py:76

models/InceptionV4.py:100

models/InceptionV4.py:114

models/InceptionV4.py:145

models/InceptionV4.py:167

models/InceptionV4.py:201

models/InceptionV4.py:228

(self, num_classes=1001)

models/InceptionV4.py:271

(self, num_classes, loss={'xent'}, **kwargs)

models/InceptionV4.py:344

(self, num_classes, loss={'xent'}, **kwargs)

models/ResNet.py:39

(self, num_classes=0, loss={'xent'}, **kwargs)

models/ResNet.py:73

(self, in_channels, e1_channels, e3_channels)

models/SqueezeNet.py:30

(self, in_channels, s1_channels, e1_channels, e3_channels)

models/SqueezeNet.py:51

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